Factors Influencing Credit Access By Small And Medium Scale Enterprises In Wa Municipality

Study Area

Chapter three entails the processes or ways, methods or approaches, tools and techniques used in the study to facilitate in assessing or evaluating the key or most important contributing factors (determinants) of financial assistance (credit) acquired (accessed) by Small and Medium Scale Enterprises in Wa Municipality. The major factors that lead to credit accessed by small and medium scale enterprises, Research Design, Study Population, Sample Size, and instruments used for Data collection as well as Data analysis techniques are all discussed in this chapter.

The study was conducted in Wa Municipal. The Region is situated in the Northern part of Ghana.

Wa Municipality has its capital as Wa, which also serves as the Regional capital of Upper West Region.

The collection and analysis of data in a logical or systematic and well-ordered or organized approach in an attempt to make or get significant or meaningful information from it is referred to as research design. (Zikmund, 2003: Jankowice, 2005). It can be said to be the plan or blueprints and framework or edifice of examination or exploitationconsidered to attain answers to the research problem. This study is premeditated to deliver an in-death or detailedprocess of surveillance, collecting or amassing, analyze or scrutinize, and interpret data by means of quantitative and qualitative research approach. This research was undertaken to define the significant factors or dynamics of credit access by small and medium scale enterprises in Wa municipality.  Quantitative as well as Descriptive research designs or schemes were used for our study. Due to the voluminous nature of the case study Descriptive research method or technique was adopted to obtain precise oraccurate reaction and results. Similarly the application of the quantitative research design or approach was also used to give a statistical, mathematical or computational view of the data that was gathered or collected out from the descriptive research. Quantitative research is referred to as the systematic or organized empirical investigation or probe of social occurrences or phenomenonthroughcomputational or mathematical, or statisticalmethods. Quantitative research method is to develop and use mathematical models, concepts or postulatesrelating to the occurrencesas well as also employed to deal with variables which was congregatedfrom the survey research viawell-thought-out or structured questionnaires.   

Population is entire collection of all observations of interest (people object or event) as defined by Burns and Burns (2008). SMEs registered to operate within Wa municipality, financial institutions operating within Wa municipality as well as non-financial institution operating in the area constituted the study population.  The population of the study encompassed of nine (9) Commercial Banks and four(4) Non-Bank Financial Institutions (NBFIs) in WA municipality. More explicitly, the study focused on NBFIs as well as commercial banks in WA municipality.  As a result of the existence of an ample number of FIs providing each institution with an equal opportunity of being represented, Wa was selected for this study and also considering the limited amount of time and available resources for this study. Principally, the number of financial and non- financial institutions comprised the following: Banks (National Investment Banks, Ghana Commercial Bank, Barclays Banks, Group Nduom, Agricultural Development Bank, Stanbic Bank, Access Bank, Beige Capital Bank, Apex Bank).  Non-Financial Bank(Pentecost credit union, Multi- credit, Wa corporate credit union, Teachers credit union). Small and Medium  Enterprises( HajiaBeidawu Enterprise, AmabiBesi Motors,IssahHabibashea butter processor, etc).

Research Design

Sampling is the process of choosing unit (for example organization or individuals) from a definitepopulace of importance so therefore, by observing the unit (sample), the specificpopulace from which they were picked or selected can be generalized (Neuman 2011). Sampling techniques is described as the systematic or organized steps the researcher implement or employ in selecting objects amongst the sample.  It also makes emphasis or stresses on the number or quantity of objective to be taken into consideration in the sample in other to make inference about the entire population. (Kothari, 2015). With regards to the limited number of banks and SMEs in the Wa municipality and since the population of staffs was relatively small, non-probability sampling was used to obtain the data. Non-probability sampling techniques suggest that the chances of an element to be chosen is unknown. Furthermore, purposive sampling was used for the interview guide. Sample size identification is the process of indicating the amount or quantity of elements tobeencompassed in a numerical sample. The sample size is asignificant characteristics of any pragmatic research within which the objective is makinginterpretation in relation to a populace from a sample. All Banks and Non-Bank financial institutions in the Wamunicipality were demarcated for the Study, based on a population of nine (9) banks and four (4) Non-Bank Financial Institutions in Wa municipality (making a total of thirteen).  However, in this case where all the institutions in the study are used there was no need to use a sampling technique. In the case of identifying the determinants of Small Scale Enterprise financing in Wa, 100 questionnaires were administered based on a simple random sampling of small scale entrepreneurs within the Wa municipality.

Research techniques are referred to as the systematic measures that can be followed to amassfacts, as well asscrutinize or examine them in order to acquire or obtain the information in which they possess (Jankowicz, 2000). Credit Officers, Credit Managers in addition toRelationship Managers within various financial institutions were presented by the management of the various financial institutions to assist us in obtaining information. We received suitable replies for the questions that was thrown to the respondents after the objective of our study or research was disclosed to them. They were guaranteed them of theconcealment of the facts being divulge to us by them and we again guaranteed them that, this would have no impact on their employment status in the organization as well as the operations of the organization in  any form. Furthermore, they were given the assurance that during the analysis their response was to be treated as a unit, and also the information provided by them was not to disclosed to any third party without seeking their consent first as well as the information they furnished us with was to be used for purposes of research only. The study also collected primary data from SME owners by the use of questionnaires.

Study Population

Two major forms of data were collected in this study, that is;

Primary data: can be said to be the data invented or created for the by the researcher for the first time viauninterrupted or straighthard work andunderstanding or skill, explicitly for the tenacity or reason of tackling his study problem. Raw data or first-hand information or data is another name conferred to this type of data. The data gathering or accumulating is under undeviating or strictregulationas well asmanagement or administration of the researcher.

Case study, personal interviews, mailed questionnaires, physical testing, and questionnaires filled and sent by enumerators, observations, survey, as well as focused group discussions, among others, are all approaches or means via which data can be collected. Structured interviews, questionnaires and surveys was the approaches employed in collecting primary data in this study.

Secondary data: On the other hand infers or suggests second-hand data or evidence which is previously or before nowassembledas well asdocumented by anybody apart from or with the exception of the user of the information for a resolution, not connecting to the present study problem. It is thereadily sort or type of data amassed or accumulated from countlessbases or fonts such as websites, internal records of the entity, reports, government publications, censuses, books, as well as journal articles, among others. It providesquite a lot ofbenefits as it is effortlessly obtainable or available, and also saves the researcher a lot of cost and time. Secondary data for this study was collected or acquired from the Wa Municipal Assembly which included information on community profile and the number of registered Small Scale Enterprises as well as FIs in Wa Municipality.

Data gathering or collection apparatuses are the tools or equipment that are used in the amassing or generation of data for the purpose of the study. Enumerated below are some of the data collection instruments that are commonly used by researchers to collect data for their study;

A questionnaire is a data gathering or accumulatingapparatus which consist of a sequence of questions and other prompts for the determination of assemblingfacts (information) from respondents. Sir Francis Galton was the inventor of questionnaires.

Interview consist of the collection of data by asking the respondents specific questions constructed by the interviewer, similarly data could begathered or amassed by listening to the individual andrecording their reactions.

Focused group is a plannedinteraction with the aim of inspiringdialogueround a precisetheme or subject.

Sample Size Determination

Small scale entrepreneurs who engage in trading activities such as, the buying and selling of goods and services and manufacturing activities were sampled using the simple random sampling method. Self-administered semi-structured questionnaire was used for the collection of primary data. Respondents who find it difficult to interpret the questions in the questionnaire mostly due to their educational level were subject to oral interview using researcher administered questionnaire. The data was collected by the members of group 166.  

Data analysis or investigation is the process or manner of thoroughly or methodically or systematically employing or using statistical and/or logical techniques or procedures to designate and demonstrate, summarize as well as review, and assess data. According to (Sharmo and Resnik 2003),numerous diagnostictechniques or processesoffer a way of making inductive interpretation from data in addition todifferentiating the indicator ( phenomena of concern) from the noise (statistical fluctuation) existent in the data. This study explicitlypursues to define or explain the convenienceheights of diverse means of funding or financial assistance to small scale enterprises in Wa municipality. There are numerous tools and techniques suitable for scrutinizing ordinal data. In this study, we used the software SPSS for Windows to do analysis and descriptive statistics, frequency tables, percentages and cross- tabulations was also used to present the results. Also, the study designated the elements that effect the likelihood or odds of small scale enterprises demanding or requesting for credit.

Limitations or restrictions in the methodology relate or link to the non-availability or nonexistence of a database or databank with precise records of SMEs both nationwide and at thelocal level. To overcome or bypass this barrier or obstacle, dependence was placed upon membership registers acquired or secured from organizations affiliated to SMEs and Municipal Assembly database. The study was also faced with financial and time resource constraints and was therefore limited to Wa, the district capital in the Wa Municipality.

The researcher’s virtuousobligation to the contributors or part-takers as well as funders of the project is of great essence (McGivern, 2006). Where there is struggle, the accomplices’ rights, as individuals, should be the utmost priority. According to Van der Wal (2006), researchers must do whatever it takes within their supremacy to shelteror safeguard thesocial, psychological as well as the physicalwell-being, as well as to honour the self-respectin addition to theseclusionor discretionof the study population. Theapproval to undertake the study, conversantconsensus and concealment (the right to discretion and shieldingproof of identity), are three categories of moral principles for surveys that aninvestigatorought to consider. The resulting guidelines focuses on how ethical issues were handled in the study.

Sampling Procedure

Chapter four deals with the exhibition and explanation orclarification of the data congregated in the field which was treated and scrutinized or analyzed using Statistical Package for Social Scientists (SPSS) and presented with frequency tables, cross tabulations, ANOVA table and Regression table.

Expatiated below is the analysis or enquiry of data obtained from thirteen respondents from 9 banks and 4 Non-Bank Financial Institutions following the interviews. Figure 1 below shows the summary of the gender composition of the employees from the banking and non-banking financial institution who participated in the study.

Figure 4.1Respondents gender summary

 

Source: Field survey, 2018

The figure above shows the total number or composition of the respondents who participated in the answering of the interview guide by the employees of the banking and non- banking financial institutions. Majority of the respondents 69% to be precise were males,whereas 31% of the respondents were females, details are shown in figure 1.

Figure 2 contains summary of approval or other wise of cost of SME loan without collateral.

Figure 4.2: premium placed on collateral by financial institutions

Source:Field survey, 2018

The interviewers wanted to know the importance of collateral to advancing credit to SMEs. Out of the thirteen institutions nine (9) demanded or required collateral or security as a principal or prime prerequisite to offer credit whereas only four werekeen to give funding to SMEsdevoid of surety. High premium is placed on the collateral for the reason that FIs were apprehensive or feared that should in case a borrower or debtor defaults on or fails to pay  a loan (as result of insolvency or extraelements or factors), the borrower forfeits or loses  the property pledged or promised as the collateral. The collateral or security serves as insurance or indemnity for the risk taken by FIs. That is, thesurety (collateral) functions as safety or safeguard against a borrower’s nonpayment or failure to service the loan for the lender. This means that collateral has a positive effect on the access to credit by SMEs in Wa municipality.

Amusingly the interview depicts that only two (2) amongst the thirteen institutions mandated or required audited accounts before granting credit to SME?s. As many as many as eleven (11) did not necessitate audited accounts in order to offer credit or funds to the SMEs. Amid this eleven (11) are all the four (4) non-bank financial institutions.

Fig 4.3: Premium placed on Audited Accounts

Source of Data

 

Source: Field survey, 2018

The figure above  reveals that two (2) of the respondents, representing 15.4% placed high premium on audited accounts, whiles the remaining eleven respondents, representing 84.6% placed low premium on audited accounts. This indicates that, audited accounts have no positive effect on the credit accessed by SMEs in Wa municipality, that is, SMEs are granted credit without audited accounts.

No	Eligibility criteria	Frequency	Percent
1	Collateral security	9	100
2	Not less than six months account operation	9	100
3	Audited account where applicable	2	22
4	Bank statements	3	33
5	Registered business and its documents.	1	11

Source: Field survey, 2018  

Table 4.1: presents data on loan eligibility requirements that was obtained from 9 banks. Out of the 9 banks, 9 (representing 100%) listed collateral security as a requirement to qualify

SMEs for loan. 9 banks (representing 100%) indicated that they would require customer’s operation of banks account for not less than six month. 2 banks (representing 22%) listed an audited accounts as part of their eligibility criteria. 3 banks (representing 33%) also included the SMEs bank statements as a criterion, registered business and its documents, and recommendation by the risk manager were each listed as criterion for loan eligibility by 1 bank, and constitute 11%.

Table 4.2: Summary of loan eligibility criteria by non-bank financial institutions

	Eligibility criteria	Frequency	Percent
1	Collateral security	4	100
2	Guarantor	4	100
3	Audited account where applicable	0	0
4	Six months Bank statements	3	75
5	Registered business and its documents.	4	100
6	Tax documents	1	25
7	Age of business existence	4	25

Source: Field survey, 2018

Table 4.2 illustrate data on loan entitlement or eligibility requirements that was obtained from four (4) Non-Bank Financial Institutions (NBFIs). All four banks (representing 100%) listed collateral security, registered business and its document, guarantor as well as age of the firm as requirement to qualify SMEs for a loan. 1 bank (representing 25%) indicated that they would require tax documents of the business. 3 banks (representing 75%) listed six months bank statement as part of their eligibility criteria.

A total of 100 responses were received, of which 8 were discovered to be inadequate or with blunders. Accurate and flawless replies added up to 92 in addition to forming the foundation for this scrutiny or breakdown.   

Frequency statistics carried out on gender indicated that majority of the 92 respondents were males, that is, 69 out of a total of 92, representing 75% , whereas the remaining 23 were females also depicting 25%. This designates that, the SMEs sector in the Wa municipality is a male dominated one. It can be concluded that there are lesser female entrepreneurs in the Wa municipality as compared to the males. (Table 4.3)

Table4.3: Frequency Distribution Table- Respondent by Gender

SEX	FRQUENCY	PERCENTAGE (%)
Male	69	75
Female	23	25
Total	92	100

Source: Field survey 2018

Table 4.4 below illustrates the age distribution of the respondents. Respondents who fall between the ages of 26-35 dominate in the registered small scale enterprises with 48.9% participation whiles those below the age of 18-25 are the least participants in the industry. The dominant age group being a part of the active working classpopulace is discovered to be in the private informal segment and mostly suchindividuals are associated with inadequate access to credit.

Table 4.4: Age Structure of the Respondent

Age Group	Frequency	Percent
18-25	10	10.9
26-35	45	48.9
36-45	23	25
46 and above	14	15.2
Total	92	100

Source; Field survey, 2018

Table4.5: Business Category of the Respondents

Category	Frequency	Percent
Manufacturing	8	8.7
Retailing	74	80.4
Service	10	10.9
Total	92	100

Source: Field survey, 2018

The registered small scale enterprises arena is dominated by retailers (80.4%) as against manufacturers and service, (8.7%) and (10.9) respectively. This can be seen from the table above. From the literature, available evidence indicates that the retail business dominates in most economies of the third world countries and because of this many credit and finance institutions direct their credit advances mainly to entrepreneurs engaged in retail businesses and least credit advances to manufacturers and other forms of businesses. This therefore indicates that the business category also have a positive effect on SMEs access to financial assistance (credit) in the Wa municipality.

Table 4.6: Academic or Professional Qualification of the Respondents

Academic Qualification	Frequency	Percent
non-formal	44	47.8
Basic	18	19.6
Secondary	19	20.7
Tertiary	11	12
Total	92	100

Source: Field survey, 2018

The table above shows the academic or professional qualification of the respondents. From the table, majority (47.8%) of the respondents is illiterates and the secondary level of education (SSCE/WASSCE) dominates among the literates (20.7%). This implies, most of the registered small scale enterprises in the Municipality are owned and managed by illiterates. This could be related to the fact that the illiteracy rate in the Wa Municipality is as high as 60.8%.

Among the factors presumed to have influence on access to funds is collateral. The researchtried to find the influence of collateral security on access to credit by SMEs by asking respondents to indicate the extent of their agreement or disagreement on based on 5-point Likert scale on different construct. To begin with, the respondents were asked to rate the extent in which several applications for credit to boost their business was declined due to lack of collateral security. Majority of the respondents (48.9%) indicated their agreement, 44.4% showed neither strongly agreement while the rest were neither agree nor disagreement. Secondly, asked if the financial institutions have changed their consideration of repayment from that of collateral, most of the respondents (64.4%) strongly agreed, 27.8% only agreed while 7.8% took a neutral position. This implies that now financial institutions are now more considerate.

Third, most of the participants (67.4%) opined a strong agreement on whether they have opted for other source of finances that demand no security from their business due to lack of the collateral security, 27.2% also agreed to the same while 5.4% were neutral. Fourth, 63% strongly agreed with the construct that financial institutions stress on the delivery of collateral security as a primeor fundamental requirement in loaning, 27.2 % recorded their agreement whilst 5.4% and 4.4% took a neutral stand and disagreement respectively. Asked whether FIs have continuouslyimplemented a risk opposedstandin the direction of small entities, with an associatedincapability to concentrate on the revenuecreatingprospective of the undertaking, when evaluating or examining the possibility of loan reimbursement, 64% agreed to this, 24% disagree while the remainder were lived to neutral stands. Finally, the respondents (54%) agreed that financial institutions are reluctant to avail credit to SMEs since they claim that they work informally while it is difficult to provide acceptable confirmation of SMEs earnings. However, 25% reported their disagreement on the same whereas a significant proportion (21%) chooses to be neutral.

This section explores the various variables that influence the credit accessed by the respondents (SMEs) in Wa Municipality. Multiple regression analysis was executed to conclude ifadequateproof or substantiation wasavailable to permit the investigators (researchers) to decide whether  there is a direct (linear) correlation or affiliation (relationship) or linear model amongst the dependent variable (Y)as well as the independent variables(s), X1, X2,…Xp-1. Testing for the linear relationship or bond amid the identified variables in the objectives of the research was done by using multiple regression for this research.

Mathematically, the linear model is stated below as;

Y_AC= β₀ + β₁X₁ + β₂X₂ + β₃X₃ +β₄X₄ + E

Where;

Y_AC = access to credit

X₁ = location of business

X₂ = ownership type

X₃ = age of firm

X₄ = firm’s size

And also where,β0, β1, β2, β3,andβ4,are unidentified or unfamiliarcoefficients whose values are predicted by the regression investigation from the SPSS outcome.

E is the chance or unplanned (random) error term with anticipated value of zero as well as variance of 1.

The linear regression analysis models analyzes the association between the reliant on (dependent) variable which is access to credit as well asautonomous (independent) variable which is location of  business. The coefficient of determination () in addition to correlation coefficient (R) demonstrates the degree of association between location of business and access to credit by SMEs in Wa Municipality. The outcomes of the linear regression specify that =0.753 as well as R= 0.868 implying there is a positive linear associationamid location of business as well asSMEs access to credit in Wa Municipality. This means the variations in the dependent variable (access to credit) is caused by the variation in the independent variable (location of business). From the objectives of our study, it means that the location of business influences the access to credit given to SMEs. That is, FIs presume that SMEs at vantage points are capable making profit to repay their loans without any problem.

Table 4.7; access to credit and location of business summary model
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.868a	.753	.750	.33225
Source: Field survey, 2018

 Table 4.8illustrates the outcomes of ANOVA test whichdivulge that location of business have substantial or essential impact on SMEs access to credit as the actual P value is less than 5% level of significance. This implies that the model Y=B0+B1X1+E is significant, therefore the null hypothesis should not be rejected.

Table 4.8: access to credit and location of business model ANOVA

Model	Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	30.282	1	30.282	274.329	.000b
Residual	9.935	90	.110
Total	40.217	91
Source: Field survey 2018

The model coefficients amongst the dependent variable (access to credit) and independent variable (location of business) as a percentage of total credits are shown in table 4.9 below. The model coefficient are positive and therefore suggests that both variables are positively related. The coefficients are significant at 5% confidence level.

Table 4.9: access to credit and location of business Model Coefficients

Model	Unstandardized Coefficients	Standardized Coefficients	T	Sig.
B	Std. Error	Beta
1	(Constant)	1.489	.061		24.286	.000
location of business and access to credit	.433	.026	.868	16.563	.000
Source: Field survey, 2018

Table 4.10: access to credit and ownership type Model summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.400a	.160	.151	.61270

Source: Field Survey.

The above table 4.11 depicts the outcome of regression model summary. The values of R and  are 0.400 and 0.160 correspondingly. The R value of 0.400 symbolizes a positive linear affiliationor bond between SMEs ownership type and access to credit. The specifies that descriptive power of the independent variables is 15%, meaning that, about 15% of the disparity or changes in access to credit is elucidated by the model Y=β0+ β2X2+E where Y is access to credit and X2 is SMEs ownership type. This means that, a percentage increase in the dependent variable is as a result of a variation in the independent variable. According to the objectives stated in this study, it is observed from this model summary that, ownership type influences the access to credit by SMEs, since it was observed in our findings that FIs finance retailers more than the other sectors of the industries. This means that there is progressivecorrespondencebetween the dependent variable (access to credit) as well as the independent variable (ownership type)

Table 4.11: access to credit and ownership type Model ANOVA

Model	Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	6.432	1	6.432	17.133	.000b
Residual	33.786	90	.375
Total	40.217	91

Source: Field Survey, 2018

The above table 4.11 illustrates the outcome of ANOVA test which indicated that, SME ownership type has a significant effect on the access to credit, since the P-value is less than 5% level of significance. Implying that, the linear regression model Y = 6.432+0.212X2+E, where X2 is the SME’s ownership type and that the model was significant. It shows there is a positive correlation between the dependent variable (access to credit) as well as the independent variable (ownership type).

4.6.3; Access to credit and age of firm

Table 4.12: access to credit and firm’s age Model summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.829a	.688	.684	.37344
Source: Field survey, 2018 The above table 4.12 displays the regression model outcome summary. The values of R and R2 are 0.829 and 0.688 separately. The R value of 0.829 signifies a positive direct (linear)bond between SMEs age as well as access to credit. The R-square specifies that descriptive power of the independent variables is 68%,  meaning that about 68% of the changes in access to credit is described or defined by the model Y=β0+ β3X3+E where Y is access to credit and X3 is SMEs age.  This regression summary suggests that, a change in the dependent variable is caused by change in the independent variable. This is to imply that, the length of operation by SMEs also determine their access to credit. This is so because FIs presume that, the long the period SMEs are in operation the least their risk of loan default.

The below table 4.13 enumerates the outcome of ANOVA test which discloses that SMEs age has substantial impact on access to credit as the P-value is less than 5% level of significance. This implies that linear regression model Y=1.390+0.498X3+E where X3 is the SMEs age and that the model was significant.

Table 4.13: access to credit and firm’s age Model ANOVA

Model	Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	27.666	1	27.666	198.388	.000b
Residual	12.551	90	.139
Total	40.217	91
Source: Field survey, 2018
Table 4.14: access to credit and firm’s age Model coefficient Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) 1.390 .077 18.051 .000 age of the firm  and access to credit .498 .035 .829 14.085 .000 Source: Field survey, 2018 The model coefficients are shown in table 4.14 above and all of them are significant. Since both the constant and the independent variable both have positive coefficients, it means that the age or length of operation of SMEs highly affects their access to credit.  Access to credit and firms size

4.6.4; Access to credit and firms size

Table 4.15: access to credit and firm’s size Model summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.478a	.229	.220	.58711

Source: Field survey, 2018

The above table 4.15 indicates the regression model outcome summary;0.478 as well as 0.229 are the values of R and respectively. The R value of 0.478 denotes a positive directcorrelation between SMEs firm’s size and access to credit. The designates that descriptive power of the independent variables is 22.9%, meaning that about 22.9% of the variation in access to credit is explained by the model Y=β0+ β4X4+E where Y is access to credit and X2 is SMEs firm’s size.

This means that the variation in the dependent variable (access to credit) is caused by a change in variation in the independent variable (firm’s size)

Table 4.16: access to credit and firm’s age Model ANOVA

Model		df	Mean Square	F	Sig.
1	Regression		1	9.194	26.672	.000b
	31.023	90	.345
Sum of Squares	40.217	91
Source: Field survey, 2018 The above table 4.16 confirms the outcome of ANOVA test which discloses that SME firm size has significant effect on access to credit since the P value is less than 5% level of significance. This implies that linear regression model Y=1.723+0.182X4+E where X4 is the SMEs firm’s size and that the model was significant.

Table 4.17: access to credit and firm’s age Model coefficients

Model	Unstandardized Coefficients	Standardized Coefficients	T	Sig.
B	Std. Error	Beta
1	(Constant)	1.723	.132		13.069	.000
firm size  and access to credit	.182	.035	.478	5.165	.000

The directconnection or associationamong the dependent variable which is access to credit as a measure of determinants of access to credit and the independent variables which are location of business, ownership type, firm’s age and firm size. This analysis is meant to attain the research general objective which was to determine the elements that influence the access to credit by small and medium sized enterprises. SMEs size, age and ownership type were included in the model as control variables. The coefficient of determination (R²) and correlation coefficient (R) were used to show the degree of relationship between the independent variables as well as access to credit (dependent variable) of SMEs in Wa municipality. An R of 0.881 shows a strong positive association between access to credit, location of business, ownership type, firm size and age of SMEs. The model developed even after accounting for independent variables, the value of R-square is still high. After controlling for the degrees of freedoms (the number of independent variables) about 77.5% of changes in access to credit is explained by the changes in the independent variables. The overall model summary confirms the objectives of this study that there is a positive connectionamong the independent variables as well as the dependent variable, because they determine the access to credit by SMEs within the Wa municipality.

Table 4.18: Overall Model summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.881a	.775	.765	.32223
Source: Field survey, 2018 Table 4.19 beneathdesignates that P-value = 0.000 is less than 5%. This demonstrates that the general model is significant and can be used in prediction and decision making. This implies that location of business, ownership type, firm’s age and firm’s size all have significant effect on access to credit by SMEs in Wa municipality. It simply means that all the indicators have a positive and significant effect on the dependent variable (access to credit), that is they all determine whether SMEs will have access to credit or not.

Table 4.19: Overall model ANOVA

Model	Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	31.184	4	7.796	75.083	.000b
Residual	9.033	87	.104
Total	40.217	91
Source: Field survey, 2018

Table 4.20 below shows the overall model coefficients all of which are significant except for the SME size. Location of business, ownership type, and age of SMEs have positive relationship, with access to credit with firm’s size having a negative relationship but is significant. Therefore, when combined with other variables, SMEs size has a significant effect on access to credit.

The model developed showing the relationship between the independent variables and access to credit is given below;

Y= 1.566 + 0.381X1 +0.003X2 + 0.159X3 – 0.087X4

 Table 4.21: Overall model coefficient

Model	Unstandardized Coefficients	Standardized Coefficients	T	Sig.
B	Std. Error	Beta
1	(Constant)	1.566	.117		13.375	.000
age of the firm  and access to credit	.159	.115	.264	1.379	.172
firm size  and access to credit	-.087	.036	-.229	-2.430	.017
ownership type and access to credit	.003	.041	.006	.077	.939
location of business and access to credit	.381	.087	.764	4.390	.000
Source: Field survey, 2018

Chapter Five

Summary Of Findings, Conclusion And Recommendations

Chapter five displays a summary of the key discoveries of the research results, inferences (conclusion) made based on the discoveries as well as recommendations establishedor centered on the objectives of the study

The rationale behind this research is to survey or scrutinize the factors or determinants of credit accessed or acquired by small scale enterprises in Wa municipality.  Respondents wereselected at a random basis and a greater number of the participants fell within 26 – 35 years age range. Out of the total sample size of 92 respondents, it was eminent that 69 of the respondents were males whereas the remaining 23 were females. A vast majority of the registered small scale enterprises engage in retailing as compared to manufacturing and services sector.

The study revealed that, respondents with little or no formal education dominated the ownership of the small scale enterprises within the Wa municipality. Also from the field survey, it was uncovered that, most of the respondents were satisfied with their access to or acquisition of credit and a greater portion of the satisfied respondents secured or obtained their credit from formal financial institutions. Only a few of the respondents were discovered through this study who complained of difficulties or hindrance in accessing credit.  

Furthermore, the education level of the respondents had less effect on their access to credit; this is for the reason that the financial institutions only cared or concerned about the loan eligibility of the respondents, thus their capability to repay or service the loan without default or failure instead of their level of education.

The t-statistics was applied to test the individual or distinct significant or important effects of the independent variables (location of business, type of ownership, firm’s age, as well as the size of the firm) on the dependent variable (access to credit). The fallouts or outcome signified that, location of the business, age of the firm, and ownership type all have significant effect on the access to credit by SMEs, whereas firm’s size  proved to be less significant in the determination of the access of credit.

Although, the t-statistics reveals firm’s size to be insignificant, further analysis using the F-test indicates all the independent variables simultaneously have significant effects on the dependent variable. Generally, the analysis of the data gathered reveals that, the coefficient of determination (R²) is 0.

775 from the regression model making it statistically significant. This means that 77.5% variations in the dependent variable can be explained by the independent variables.

5.2 Conclusion

According to the findings, the resultingassumptions can be drawn. Firstly, it has been gathered or understood that, retailing, service as well as manufacturing undertakings are the key or foremostmonetary activities amid the listed or registered small scale enterprises in the Wa municipality,nonetheless other petty economic undertakingslikewise exists on the other hand.

It is evident or apparent that, access to finance has a lot of confident economic influence oreffect on the sustainability of small scale businesses. For example it functions as opening capital and resource for business enlargement or growth to numerous entrepreneurs in Wa municipality.

Majority of the listed small scale are contentor pleased with the access to credit delivered to them by the financial institutions. Nonetheless, a fewhitches or complications have been noted or identified to be related with the methods and techniques involved in accessing credit, such as collateral requirement, bureaucratic processes and so on.

Relying on the discoveries it was disclosed that heights (level) of education, location of the business, collateral, type of ownership, firm’s age, as well as the firm’s size were all regarded as the key factors or determinants of access to credit by small scale enterprises in Wa municipality.

Centered on the results or discoveries of the research, quite a few of recommendations or references could be enumerated to support policy architects or legislators and implementers as well as growthconcernedestablishments and individuals to tackle or help solve  the difficulties that small scale enterprises in the Wa municipality have been stumbling  upon or chance upon.

First and foremost, the establishment of credit conveniences in the manner of loans to entrepreneurs of small scale enterprises to participate in economic undertakings should be given primacythought. This can be put in place by the government and the private sectors, thereby crafting a favorableatmosphere or setting for the industrialists to have access to the credit from the FIsdevoid ofstringent collateral terms. Non-governmental organizations (NGOs)as well as the ministry of finance and economic planning can also be of prominentassistance in this course.

Secondly, it is the necessary for credit or financialorganizations to undergo on public tutelage in the form of publicmediums, workshops, conferences, sessions and media outreach to update or notify or enlighten small scale enterprise machinists on the procedures, necessities and methods involved in acquiring or obtainingcredits in order to augment their petition for credit and discard the false impressionaround credit attainment.

In addition, credit establishmentsought tocarry outampleinvestigation and improvementin the direction ofaugmenting their lending products. This encompasses enhancing the loan terms to fit customers’array of cash flow, realistic interest ratio, and satisfactory loan sum in addition tojudiciouscosts charged for handling of loans. This will ultimatelyassist small scale entrepreneurs to meet their loan reimbursement and also lessen the degree of nonpayment in order to sustain the maneuvers of credit organizations in the Wa Municipality.

Furthermore, the National Board for Small Scale Industries (NBSSI) and Microfinance and Small Scale Centers (MASLOC) mustspread out their tentacles to stretch out to supplementary small scale enterprises and also reinforce their delegationschemes or coordination to simplifyconvenience of credit.

Finally, legal action could be seen as effective recommendation to credit institutions as a panacea for retrieving default loans and loans past due.

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